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Ein globales Gespräch über Native-Speakerism: Hin zur Förderung der Vielfalt im Englischunterricht
No full textEs sind über drei Jahrzehnte vergangen, seit Paikedays „The Native SpeakerNative speaker is Dead“ veröffentlicht wurde, aber leider ist das Native-Speaker-Konstrukt (der Glaube, dass Muttersprachler die idealen Sprachlehrer sind) immer noch sehr lebendig und bleibt im Bereich des Englischunterrichts dominant. Obwohl das Bewusstsein für sprachliche Diskriminierung und die Rassifizierung des Englischen in akademischen Kreisen weit verbreitet ist, sind solche Ungerechtigkeiten vor Ort immer noch häufig, wie man sehen kann, wenn man Stellenanzeigen durchsucht, die oft direkt „Native Speaker“ (NS) verlangen. Dieses Kapitel beginnt mit einer Reihe von autoethnografischen Berichten über die Erfahrungen des Autors mit dem Native-Speakerism weltweit. Diese Berichte dienen als Sprungbrett für eine multiple Fallstudie, die die Einstellungen und Erfahrungen von 130 erwachsenen Lernenden und 72 Englischlehrern in zwei mehrsprachigen und multikulturellen Städten: VancouverVancouver, KanadaCanadaund Abu DhabiAbu Dhabi, Vereinigte Arabische EmirateUnited Arab Emirates untersucht. Die Ergebnisse zeigten, dass der Native-Speakerismus sowohl in den Ideologien der Schüler als auch in den Erfahrungen der Lehrer bei der Rekrutierung und im Klassenzimmer stark präsent ist. Es wird argumentiert, dass der sprachliche und ethnische Hintergrund der Lehrer oft ihre Erfahrungen stark beeinflusst. Aus der Perspektive der World EnglishesWorld Englishes werden die binären Begriffe „Native Speaker/Non-native Speaker (NNS)“ aufgrund der komplexen Zusammensetzung der heutigen Englischsprecher problematisiert. Es werden praktische Wege vorgeschlagen, um über die NS/NNS-Binaritäten hinauszugehen und Vielfalt im Englischunterricht zu fördern
Chapter 9 Discovering and analyzing IoT-cloud vulnerabilities
No full textThe intricate and evolving IoT-cloud vulnerability instances highlight the critical importance of proactive management in these vulnerabilities in such interconnected environments. We explore the inherent predisposition of IoT-cloud systems to a range of vulnerabilities, including software flaws, hardware limitations, and network weaknesses. The chapter emphasizes the necessity of identifying and understanding these vulnerabilities to safeguard the digital transformation enabled by the cloud-to-things continuum. A detailed examination of various methodologies for vulnerability discovery is presented, considering both automated and manual approaches, while illustrating the role of specialized tools and testbeds in identifying potential security gaps. The discussion extends to analyzing and classifying vulnerabilities, underscoring the importance of assessing their severity and impact through standardized frameworks, such as the common vulnerabilities and exposures (CVE) system. We discuss effective strategies for managing these vulnerabilities through models and practices for continuous monitoring, regular updates, and patch management. Typical case studies in diverse sectors, such as healthcare, smart cities, and industrial IoT, are presented to illustrate the practical challenges and solutions in IoT-cloud security, offering insights into both the detection and resolution of security issues. We conclude with an outlook on future trends and challenges, reflecting on the dynamic nature of IoT-cloud vulnerabilities and the evolving cybersecurity landscape. We show the need for adaptive and anticipative security strategies to address emerging threats and maintain the trustworthiness in IoT-cloud ecosystems
Chapter 11 Smart citizenship through IoT-cloud adoption
No full textThis chapter examines smart citizenship within the context of IoT and cloud adoption in smart cities. The foundations for fostering smart citizenship are outlined, including critical skills, knowledge, and digital smart competencies needed to thrive in these contexts. Educational models using u-learning and ambient learning spaces to develop these competencies are discussed. Practical initiatives and case studies from Barcelona, Amsterdam, South Korea, and Singapore illustrate real-world applications for advancing smart citizenship through civic engagement in IoT and cloud technologies. Societal challenges related to privacy, security, the digital divide, and ethical deployment are addressed. The chapter advocates for inclusive policies, continuous education, and community engagement to empower citizens in future smart cities
Chapter 16 IoT-cloud business case studies and investment roadmap for sustainable digital transformation
No full textThis chapter is based on practical applications and strategic investments in IoT and cloud technologies, showcasing their transformative impact across various sectors. Investments that are agile, scalable, and aligned with strategic goals are shown to be essential for paving the way into the integration of emerging technologies. A proposed investment roadmap that guides stakeholders through the evolving digital landscape is revealed to ensure immediate benefits and long-term organizational success within a rapidly changing technological environment
Enhancing data classification using locally informed weighted k-nearest neighbor algorithm
No full textIn this work, a novel locally informed weighted kNN algorithm (LIWkNN) is presented to reduce the detrimental impact of outliers and an imbalanced class. The LIWkNN considers the labels of both the query and its neighboring data points, emphasizing its focus on the vicinity of the query point, enabling it to capture local patterns and variations. The algorithm updates the weights assigned to the neighbors by comparing their labels, which are subsequently utilized in the next step to predict the label for the query. Initially, all training point weights are set to 1. Secondly, predictions are made using the conventional KNN classifier, and then it is verified that the prediction matches the query label in the test data. These weights will be updated if the predicted label differs from the actual query\u27s label, which otherwise will not be changed. According to the weight update process, an outlier\u27s influence on the classification in the weighted KNN is kept to the minimum extent during the classification process, specifically if it is frequently selected as a neighbor for various queries. Thirdly, to address class imbalance, this method adjusts the weighting based on class density, ensuring that minority class points predominantly receive neighbors from their own class. Finally, once this weight update process is complete, the proposed KNN will be working with the final weights to classify the test points. The LIWkNN\u27s competitive performance and straightforward architecture demonstrate the model\u27s novelty, setting it apart from its cutting-edge competitors. To validate the LIWkNN\u27s generalizability on a broader range of datasets, a comprehensive assessment using five evaluation measures (accuracy, F1-measure, ROC, mean absolute error—MAE, and geometric mean—GM) across sixty (balanced, imbalanced, noisy, time-series, and images) datasets is carried out in six experimental phases. According to the results supported with a multi-criteria analysis, LIWkNN is significantly more promising over the vast majority of all datasets taken into consideration, both generally and for specific k values
A review on the use of immersive technology in space research
Full text linkImmersive technologies, such as virtual reality (VR), augmented reality (AR), and mixed reality (MR), create digital experiences by merging real and virtual worlds, offering enhanced spatial engagement and sensory immersion. This study examines immersive technologies’ possible advancements to space research, along with application examples in data visualization, astronaut training, and mission planning. Based on the analysis of 44 papers, immersive technologies can assist in diverse areas as varied as procedure guidance, astronaut training, and health-related aspects involving using devices such as HTC Vive, Microsoft HoloLens, and Oculus. The most critical challenges are, by far, difficulties in the selection of participants for research, authenticity in the representation of virtual environments, and technical problems. Overall, our findings indicate the utility of immersive systems through enhanced effects, mostly in training, data interpretation, and human-machine interaction. Future research should involve the continuation of studies using larger sample sizes, interoperability, computational power, user comfort, and machine learning
An Intelligent Medical Model for Classification of Brain Tumours and Stroke Lesions Using Machine Learning in Healthcare for Resource-Constrained Devices
No full textBrain tumors and stroke lesions are major health problems and need to be diagnosed on time and accurately to improve patient outcomes. Nevertheless, resource-constrained devices like portable medical equipment and embedded systems are limited by computational resources that prevent the deployment of sophisticated deep-learning models. We address the problem of efficient and accurate brain tumor and stroke lesion classification on such devices. To realize this, we present a light weight deep learning framework using MobileNetV3 for feature extraction and a hybrid optimization algorithm based on Bat Algorithm (BA) and Differential Evolution (DE) to fine-tune the hyper parameters. The framework relies on quantization techniques and a compressed representation of medical images to reduce memory and computational overhead. Brain tumor Magnetic Resonance Imaging (MRI) scan datasets (BraTS 2021), stroke lesion datasets (ISLES 2022), and others were preprocessed with normalization and data augmentation to be robust. To evaluate the performance of the model under limited conditions, it was deployed on Raspberry Pi 4 and other edge devices. The proposed framework achieves an accuracy of 96.3% for brain tumor classification and 94.8% for stroke lesison detection, with an inference time of 2.4 s per image on Raspberry Pi 4, which outperforms state-of-the-art methods in both accuracy and computational efficiency. Furthermore, the hybrid BA-DE optimization reduced model size by 28% without significant loss of accuracy. This study demonstrates that the proposed lightweight framework effectively balances accuracy and computational efficiency, making it suitable for real-time applications in resource-constrained healthcare environments. The results highlight its potential to empower low-resource medical facilities with advanced diagnostic capabilities
Psychometric properties of the Depression, Anxiety, and Stress Scale–21 (DASS-21) across nine countries/regions
No full textExaminations of the internal structure of the Depression, Anxiety, and Stress Scale-21 (DASS-21) have yielded inconsistent conclusions within and across cultural contexts. This study examined the dimensionality and reliability of the DASS-21 across three theoretically plausible factor structures (i.e., unidimensional, oblique three-factor, and bifactor) as well as measurement equivalence/invariance of the DASS-21 using two different approaches (i.e., multigroup confirmatory factor analysis and the alignment approach) with a large, diverse sample of 2,920 young adult college student participants from nine countries/regions (i.e., Australia, Brazil, Germany, Hong Kong, Lithuania, Taiwan, Türkiye, United Arab Emirates, and the United States). Results showed an excellent fit of the bifactor model in all countries/regions except the UAE and the US in which the model did not converge. Regarding parameter equivalence, we found configural, threshold, and loading invariance for the oblique three-factor model (across the nine studied countries/regions) and for the bifactor model (across seven countries/regions). Results indicate that DASS-21 scores measure a general psychological distress factor with more validity and reliability than depression, anxiety, or stress constructs independently. Findings supported the bifactor structure of DASS-21 and demonstrated that cross-cultural comparisons using this scale should be conducted using proper procedures, such as the alignment approach
Ransomware crime through the lens of neutralisation theory
No full textThis study examines ransomware crime through the lens of neutralisation theory, and explores techniques used by alleged offenders to justify their involvement in ransomware attacks. This work focuses on highly organised ransomware groups that not only conduct attacks but also operate as Ransomware-as-a-Service businesses. The interview data (n = 9) used in this research were collected by several media and cyber security companies. Drawing on Kaptein and Van Helvoort model of neutralisation techniques, we discovered that interviewees – reported ransomware offenders – distorted the facts (n = 5) and negated societal norms (n = 8). Less common, some interviewees admitted breaking norms, but they rejected responsibility by blaming the circumstances (n = 8) or their own shortcomings (n = 3). These results offer new insights that can support the development of counter-narratives